{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n",
    "# not use this file except in compliance with the License. You may obtain\n",
    "# a copy of the License at\n",
    "#\n",
    "#      http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n",
    "# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n",
    "# License for the specific language governing permissions and limitations\n",
    "# under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Scalable Recommender with Apache Spark & Elasticsearch\n",
    "\n",
    "In this notebook, you will create a recommendation engine using Spark and Elasticsearch. Using some movie rating data,\n",
    "you will train a collaborative filtering model in Spark and export the trained model to Elasticsearch. Once exported, \n",
    "you can test your recommendations by querying Elasticsearch and displaying the results.\n",
    "\n",
    "### _Prerequisites_\n",
    "\n",
    "The notebook assumes you have installed Elasticsearch, Apache Spark and the Elasticsearch Spark connector detailed in the [setup steps](https://github.com/IBM/elasticsearch-spark-recommender/tree/master#steps).\n",
    "\n",
    "> _Optional:_\n",
    "\n",
    "> In order to display the images in the recommendation demo, you will need to access [The Movie Database (TMdb) API](https://www.themoviedb.org/documentation/api). Please follow the [instructions](https://developers.themoviedb.org/3/getting-started) to get an API key.\n",
    "\n",
    "## Overview\n",
    "\n",
    "You will work through the following steps\n",
    "\n",
    "1. Prepare the data\n",
    "2. Use the Elasticsearch Spark connector to save it to Elasticsearch\n",
    "3. Load ratings data and train a collaborative filtering recommendation model using Spark MLlib\n",
    "3. Save the model to Elasticsearch\n",
    "4. Show recommendations using Elasticsearch's script score query together with vector functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Prepare the data\n",
    "\n",
    "* This notebook uses the \"small\" version of the latest MovieLens movie rating dataset, containing about 100,000 ratings, 9,000 movies and 700 users\n",
    "* The latest version of the data can be downloaded at https://grouplens.org/datasets/movielens/latest/\n",
    "* Follow the [Code Pattern instructions](https://github.com/IBM/elasticsearch-spark-recommender/tree/master#5-download-the-data) to download the `ml-latest-small.zip` file and unzip it to a suitable location on your system.\n",
    "\n",
    "The folder should contain a number of CSV files. We will be using the following files:\n",
    "* `ratings.csv` - movie rating data\n",
    "* `links.csv` - external database ids for each movie\n",
    "* `movies.csv` - movie title and genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "                <p><b>SparkSession - hive</b></p>\n",
       "                \n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://nicks-mbp-3.dlink:4040\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v2.4.5</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[*]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>PySparkShell</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        \n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "<pyspark.sql.session.SparkSession at 0x111fd7c10>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first import a few utility methods that we'll use later on\n",
    "from IPython.display import Image, HTML, display\n",
    "# check PySpark is running\n",
    "spark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load rating and movie data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ratings**\n",
    "\n",
    "The ratings data consists of around 100,000 ratings given by users to movies. Each row of the `DataFrame` consists of a `userId`, `movieId` and `timestamp` for the event, together with the `rating` given by the user to the movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of ratings: 100836\n",
      "Sample of ratings:\n",
      "+------+-------+------+---------+\n",
      "|userId|movieId|rating|timestamp|\n",
      "+------+-------+------+---------+\n",
      "|     1|      1|   4.0|964982703|\n",
      "|     1|      3|   4.0|964981247|\n",
      "|     1|      6|   4.0|964982224|\n",
      "|     1|     47|   5.0|964983815|\n",
      "|     1|     50|   5.0|964982931|\n",
      "+------+-------+------+---------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# if you unzipped the data to a different location than that specified in the Code Pattern setup steps\n",
    "# you can change the path below to point to the correct location\n",
    "PATH_TO_DATA = \"../data/ml-latest-small\"\n",
    "# load ratings data\n",
    "ratings = spark.read.csv(PATH_TO_DATA + \"/ratings.csv\", header=True, inferSchema=True)\n",
    "ratings.cache()\n",
    "print(\"Number of ratings: {}\".format(ratings.count()))\n",
    "print(\"Sample of ratings:\")\n",
    "ratings.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will see that the `timestamp` field is a UNIX timestamp in seconds. Elasticsearch takes timestamps in milliseconds, so you will use some `DataFrame` operations to convert the timestamps into milliseconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+-------+------+------------+\n",
      "|userId|movieId|rating|   timestamp|\n",
      "+------+-------+------+------------+\n",
      "|     1|      1|   4.0|964982703000|\n",
      "|     1|      3|   4.0|964981247000|\n",
      "|     1|      6|   4.0|964982224000|\n",
      "|     1|     47|   5.0|964983815000|\n",
      "|     1|     50|   5.0|964982931000|\n",
      "+------+-------+------+------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ratings = ratings.select(\n",
    "    ratings.userId, ratings.movieId, ratings.rating, (ratings.timestamp.cast(\"long\") * 1000).alias(\"timestamp\"))\n",
    "ratings.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Movies**\n",
    "\n",
    "The file `movies.csv` contains the `movieId`, `title` and `genres` for each movie. As you can see, the `genres` field is a bit tricky to use, as the genres are in the form of one string delimited by the `|` character: `Adventure|Animation|Children|Comedy|Fantasy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw movie data:\n",
      "+-------+----------------------------------+-------------------------------------------+\n",
      "|movieId|title                             |genres                                     |\n",
      "+-------+----------------------------------+-------------------------------------------+\n",
      "|1      |Toy Story (1995)                  |Adventure|Animation|Children|Comedy|Fantasy|\n",
      "|2      |Jumanji (1995)                    |Adventure|Children|Fantasy                 |\n",
      "|3      |Grumpier Old Men (1995)           |Comedy|Romance                             |\n",
      "|4      |Waiting to Exhale (1995)          |Comedy|Drama|Romance                       |\n",
      "|5      |Father of the Bride Part II (1995)|Comedy                                     |\n",
      "+-------+----------------------------------+-------------------------------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# load raw data from CSV\n",
    "raw_movies = spark.read.csv(PATH_TO_DATA + \"/movies.csv\", header=True, inferSchema=True)\n",
    "print(\"Raw movie data:\")\n",
    "raw_movies.show(5, truncate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For indexing into Elasticsearch, we would prefer to represent the genres as a list. Create a `DataFrame` user-defined function (UDF) to extract this delimited string into a list of genres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----------------------------------+-------------------------------------------------+\n",
      "|movieId|title                             |genres                                           |\n",
      "+-------+----------------------------------+-------------------------------------------------+\n",
      "|1      |Toy Story (1995)                  |[adventure, animation, children, comedy, fantasy]|\n",
      "|2      |Jumanji (1995)                    |[adventure, children, fantasy]                   |\n",
      "|3      |Grumpier Old Men (1995)           |[comedy, romance]                                |\n",
      "|4      |Waiting to Exhale (1995)          |[comedy, drama, romance]                         |\n",
      "|5      |Father of the Bride Part II (1995)|[comedy]                                         |\n",
      "+-------+----------------------------------+-------------------------------------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import udf\n",
    "from pyspark.sql.types import *\n",
    "# define a UDF to convert the raw genres string to an array of genres and lowercase\n",
    "extract_genres = udf(lambda x: x.lower().split(\"|\"), ArrayType(StringType()))\n",
    "# test it out\n",
    "raw_movies.select(\"movieId\", \"title\", extract_genres(\"genres\").alias(\"genres\")).show(5, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, that looks better!\n",
    "\n",
    "You may also notice that the movie titles contain the year of release. It would be useful to have that as a field in your search index for filtering results (say you want to filter our recommendations to include only more recent movies).\n",
    "\n",
    "Create a UDF to extract the release year from the title using a Python regular expression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Jumanji', '1995')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "# define a UDF to extract the release year from the title, and return the new title and year in a struct type\n",
    "def extract_year_fn(title):\n",
    "    result = re.search(\"\\(\\d{4}\\)\", title)\n",
    "    try:\n",
    "        if result:\n",
    "            group = result.group()\n",
    "            year = group[1:-1]\n",
    "            start_pos = result.start()\n",
    "            title = title[:start_pos-1]\n",
    "            return (title, year)\n",
    "        else:\n",
    "            return (title, 1970)\n",
    "    except:\n",
    "        print(title)\n",
    "\n",
    "extract_year = udf(extract_year_fn,\\\n",
    "                   StructType([StructField(\"title\", StringType(), True),\\\n",
    "                               StructField(\"release_date\", StringType(), True)]))\n",
    "    \n",
    "# test out our function\n",
    "s = \"Jumanji (1995)\"\n",
    "extract_year_fn(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok the function works! Now create a new `DataFrame` with the cleaned-up titles, release dates and genres of the movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleaned movie data:\n",
      "+-------+---------------------------+------------+-------------------------------------------------+\n",
      "|movieId|title                      |release_date|genres                                           |\n",
      "+-------+---------------------------+------------+-------------------------------------------------+\n",
      "|1      |Toy Story                  |1995        |[adventure, animation, children, comedy, fantasy]|\n",
      "|2      |Jumanji                    |1995        |[adventure, children, fantasy]                   |\n",
      "|3      |Grumpier Old Men           |1995        |[comedy, romance]                                |\n",
      "|4      |Waiting to Exhale          |1995        |[comedy, drama, romance]                         |\n",
      "|5      |Father of the Bride Part II|1995        |[comedy]                                         |\n",
      "+-------+---------------------------+------------+-------------------------------------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "movies = raw_movies.select(\n",
    "    \"movieId\", extract_year(\"title\").title.alias(\"title\"),\\\n",
    "    extract_year(\"title\").release_date.alias(\"release_date\"),\\\n",
    "    extract_genres(\"genres\").alias(\"genres\"))\n",
    "print(\"Cleaned movie data:\")\n",
    "movies.show(5, truncate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, join the `links.csv` data to `movies` so that there is an id for _The Movie Database_ corresponding to each movie. You can use this id to retrieve movie poster images when displaying your recommendations later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleaned movie data with tmdbId links:\n",
      "+-------+---------------------------+------------+-------------------------------------------------+------+\n",
      "|movieId|title                      |release_date|genres                                           |tmdbId|\n",
      "+-------+---------------------------+------------+-------------------------------------------------+------+\n",
      "|1      |Toy Story                  |1995        |[adventure, animation, children, comedy, fantasy]|862   |\n",
      "|2      |Jumanji                    |1995        |[adventure, children, fantasy]                   |8844  |\n",
      "|3      |Grumpier Old Men           |1995        |[comedy, romance]                                |15602 |\n",
      "|4      |Waiting to Exhale          |1995        |[comedy, drama, romance]                         |31357 |\n",
      "|5      |Father of the Bride Part II|1995        |[comedy]                                         |11862 |\n",
      "+-------+---------------------------+------------+-------------------------------------------------+------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "link_data = spark.read.csv(PATH_TO_DATA + \"/links.csv\", header=True, inferSchema=True)\n",
    "# join movies with links to get TMDB id\n",
    "movie_data = movies.join(link_data, movies.movieId == link_data.movieId)\\\n",
    "    .select(movies.movieId, movies.title, movies.release_date, movies.genres, link_data.tmdbId)\n",
    "num_movies = movie_data.count()\n",
    "print(\"Cleaned movie data with tmdbId links:\")\n",
    "movie_data.show(5, truncate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **_Optional_**\n",
    ">\n",
    "> Run the below cell to test your access to TMDb API. You should see the _Toy Story_ movie poster displayed inline.\n",
    ">\n",
    "> To install the Python package run `pip install tmdbsimple` in your console (see the [Code Pattern Steps](https://github.com/IBM/elasticsearch-spark-recommender/tree/master#6-launch-the-notebook))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully imported tmdbsimple!\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 200
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    import tmdbsimple as tmdb\n",
    "    import json\n",
    "    from requests.exceptions import HTTPError\n",
    "    # replace this variable with your actual TMdb API key\n",
    "    tmdb.API_KEY = 'YOUR_API_KEY'\n",
    "    print(\"Successfully imported tmdbsimple!\")\n",
    "    # base URL for TMDB poster images\n",
    "    IMAGE_URL = 'https://image.tmdb.org/t/p/w500'\n",
    "    movie_id = movie_data.first().tmdbId\n",
    "    movie_info = tmdb.Movies(movie_id).info()\n",
    "    movie_poster_url = IMAGE_URL + movie_info['poster_path']\n",
    "    display(Image(movie_poster_url, width=200))\n",
    "except ImportError:\n",
    "    print(\"Cannot import tmdbsimple as it is not installed, no movie posters will be displayed!\")\n",
    "except HTTPError as e:\n",
    "    if e.response.status_code == 401:\n",
    "        j = json.loads(e.response.text)\n",
    "        print(\"TMdb API call failed: {}\".format(j['status_message']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Load data into Elasticsearch\n",
    "\n",
    "Now that you have your dataset processed and prepared, you will load it into Elasticsearch.\n",
    "\n",
    "_Note:_ for the purposes of this demo notebook you have started with an existing example dataset and will load that into Elasticsearch. In practice you may write your event data as well as user and item metadata from your application directly into Elasticsearch.\n",
    "\n",
    "First test that your Elasticsearch instance is running and you can connect to it using the Python Elasticsearch client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Nicks-MBP-3.Dlink',\n",
       " 'cluster_name': 'elasticsearch',\n",
       " 'cluster_uuid': 'FyKHMnL5T6eJh_odEf8Keg',\n",
       " 'version': {'number': '7.6.1',\n",
       "  'build_flavor': 'default',\n",
       "  'build_type': 'tar',\n",
       "  'build_hash': 'aa751e09be0a5072e8570670309b1f12348f023b',\n",
       "  'build_date': '2020-02-29T00:15:25.529771Z',\n",
       "  'build_snapshot': False,\n",
       "  'lucene_version': '8.4.0',\n",
       "  'minimum_wire_compatibility_version': '6.8.0',\n",
       "  'minimum_index_compatibility_version': '6.0.0-beta1'},\n",
       " 'tagline': 'You Know, for Search'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from elasticsearch import Elasticsearch\n",
    "\n",
    "# test your ES instance is running\n",
    "es = Elasticsearch()\n",
    "es.info(pretty=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Elasticsearch indices, with mappings for users, movies and rating events\n",
    "\n",
    "In Elasticsearch, an \"index\" is roughly similar to a \"database\" or \"database table\". The schema for an index is called an index mapping.\n",
    "\n",
    "While Elasticsearch supports dynamic mapping, it's advisable to specify the mapping explicitly when creating an index if you know what your data looks like.\n",
    "\n",
    "For the purposes of your recommendation engine, this is also necessary so that you can specify the vector field that will hold the recommendation \"model\" (that is, the factor vectors). When creating a vector field, you need to provide the dimension of the vector explicitly, so it cannot be a dynamic mapping.\n",
    "\n",
    "> _Note_ This notebook does not go into detail about the underlying scoring mechanism or the relevant Elasticsearch internals. See the talks and slides in the [Code Pattern Links section](https://github.com/IBM/elasticsearch-spark-recommender/blob/master/README.md#links) for more detail.\n",
    "\n",
    "__References:__\n",
    "* [Create index API](https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-create-index.html)\n",
    "* [Index mapping](https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping.html)\n",
    "* [Dense vector types](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **_Optional_**\n",
    "\n",
    "> If you are re-running the notebook and have previously created the `movies`, `users` and `ratings`indices in Elasticsearch, you should first delete them by un-commenting and running the next cell, before running the index creation cell that follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# es.indices.delete(index=\"ratings,users,movies\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you're ready to create your indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created indices:\n",
      "{'acknowledged': True, 'shards_acknowledged': True, 'index': 'ratings'}\n",
      "{'acknowledged': True, 'shards_acknowledged': True, 'index': 'users'}\n",
      "{'acknowledged': True, 'shards_acknowledged': True, 'index': 'movies'}\n"
     ]
    }
   ],
   "source": [
    "# set the factor vector dimension for the recommendation model\n",
    "VECTOR_DIM = 20\n",
    "\n",
    "create_ratings = {\n",
    "    # this mapping definition sets up the fields for the rating events\n",
    "    \"mappings\": {\n",
    "        \"properties\": {\n",
    "            \"timestamp\": {\n",
    "                \"type\": \"date\"\n",
    "            },\n",
    "            \"userId\": {\n",
    "                \"type\": \"integer\"\n",
    "            },\n",
    "            \"movieId\": {\n",
    "                \"type\": \"integer\"\n",
    "            },\n",
    "            \"rating\": {\n",
    "                \"type\": \"double\"\n",
    "            }\n",
    "        }  \n",
    "    }\n",
    "}\n",
    "\n",
    "create_users = {\n",
    "    # this mapping definition sets up the metadata fields for the users\n",
    "    \"mappings\": {\n",
    "        \"properties\": {\n",
    "            \"userId\": {\n",
    "                \"type\": \"integer\"\n",
    "            },\n",
    "            # the following fields define our model factor vectors and metadata\n",
    "            \"model_factor\": {\n",
    "                \"type\": \"dense_vector\",\n",
    "                \"dims\" : VECTOR_DIM\n",
    "            },\n",
    "            \"model_version\": {\n",
    "                \"type\": \"keyword\"\n",
    "            },\n",
    "            \"model_timestamp\": {\n",
    "                \"type\": \"date\"\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "create_movies = {\n",
    "    # this mapping definition sets up the metadata fields for the movies\n",
    "    \"mappings\": {\n",
    "        \"properties\": {\n",
    "            \"movieId\": {\n",
    "                \"type\": \"integer\"\n",
    "            },\n",
    "            \"tmdbId\": {\n",
    "                \"type\": \"keyword\"\n",
    "            },\n",
    "            \"genres\": {\n",
    "                \"type\": \"keyword\"\n",
    "            },\n",
    "            \"release_date\": {\n",
    "                \"type\": \"date\",\n",
    "                \"format\": \"year\"\n",
    "            },\n",
    "            # the following fields define our model factor vectors and metadata\n",
    "            \"model_factor\": {\n",
    "                \"type\": \"dense_vector\",\n",
    "                \"dims\" : VECTOR_DIM\n",
    "            },\n",
    "            \"model_version\": {\n",
    "                \"type\": \"keyword\"\n",
    "            },\n",
    "            \"model_timestamp\": {\n",
    "                \"type\": \"date\"\n",
    "            }          \n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "# create indices with the settings and mappings above\n",
    "res_ratings = es.indices.create(index=\"ratings\", body=create_ratings)\n",
    "res_users = es.indices.create(index=\"users\", body=create_users)\n",
    "res_movies = es.indices.create(index=\"movies\", body=create_movies)\n",
    "\n",
    "print(\"Created indices:\")\n",
    "print(res_ratings)\n",
    "print(res_users)\n",
    "print(res_movies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Ratings and Movies DataFrames into Elasticsearch\n",
    "\n",
    "First you will write the ratings data to Elasticsearch. Notice that you can simply use the Spark Elasticsearch connector to write a `DataFrame` with the native Spark datasource API by specifying `format(\"es\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe count: 100836\n",
      "ES index count:  100836\n"
     ]
    }
   ],
   "source": [
    "# write ratings data\n",
    "ratings.write.format(\"es\").save(\"ratings\")\n",
    "num_ratings_es = es.count(index=\"ratings\")['count']\n",
    "num_ratings_df = ratings.count()\n",
    "# check write went ok\n",
    "print(\"Dataframe count: {}\".format(num_ratings_df))\n",
    "print(\"ES index count:  {}\".format(num_ratings_es))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'took': 1,\n",
       " 'timed_out': False,\n",
       " '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0},\n",
       " 'hits': {'total': {'value': 10000, 'relation': 'gte'},\n",
       "  'max_score': 1.0,\n",
       "  'hits': [{'_index': 'ratings',\n",
       "    '_type': '_doc',\n",
       "    '_id': 'MWMJNXEBB1UPzqtXWiVE',\n",
       "    '_score': 1.0,\n",
       "    '_source': {'userId': 1,\n",
       "     'movieId': 1,\n",
       "     'rating': 4.0,\n",
       "     'timestamp': 964982703000}},\n",
       "   {'_index': 'ratings',\n",
       "    '_type': '_doc',\n",
       "    '_id': 'MmMJNXEBB1UPzqtXWiVE',\n",
       "    '_score': 1.0,\n",
       "    '_source': {'userId': 1,\n",
       "     'movieId': 3,\n",
       "     'rating': 4.0,\n",
       "     'timestamp': 964981247000}},\n",
       "   {'_index': 'ratings',\n",
       "    '_type': '_doc',\n",
       "    '_id': 'M2MJNXEBB1UPzqtXWiVE',\n",
       "    '_score': 1.0,\n",
       "    '_source': {'userId': 1,\n",
       "     'movieId': 6,\n",
       "     'rating': 4.0,\n",
       "     'timestamp': 964982224000}}]}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test things out by retrieving a few rating event documents from Elasticsearch\n",
    "es.search(index=\"ratings\", q=\"*\", size=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since you've indexed the rating event data into Elasticsearch, you can use all the capabilities of a search engine to query the data. For example, you could count the number of ratings events in a given date range using Elasticsearch's date math in a query string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count': 952,\n",
       " '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0}}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es.count(index=\"ratings\", q=\"timestamp:[2018-01-01 TO 2018-02-01]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next write the movie metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie DF count: 9742\n",
      "ES index count: 9742\n"
     ]
    }
   ],
   "source": [
    "# write movie data, specifying the DataFrame column to use as the id mapping\n",
    "movie_data.write.format(\"es\").option(\"es.mapping.id\", \"movieId\").save(\"movies\")\n",
    "num_movies_df = movie_data.count()\n",
    "num_movies_es = es.count(index=\"movies\")['count']\n",
    "# check load went ok\n",
    "print(\"Movie DF count: {}\".format(num_movies_df))\n",
    "print(\"ES index count: {}\".format(num_movies_es))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again you can harness the power of search to query the movie metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'took': 2,\n",
       " 'timed_out': False,\n",
       " '_shards': {'total': 1, 'successful': 1, 'skipped': 0, 'failed': 0},\n",
       " 'hits': {'total': {'value': 3, 'relation': 'eq'},\n",
       "  'max_score': 9.46188,\n",
       "  'hits': [{'_index': 'movies',\n",
       "    '_type': '_doc',\n",
       "    '_id': '2571',\n",
       "    '_score': 9.46188,\n",
       "    '_source': {'movieId': 2571,\n",
       "     'title': 'Matrix, The',\n",
       "     'release_date': '1999',\n",
       "     'genres': ['action', 'sci-fi', 'thriller'],\n",
       "     'tmdbId': 603}},\n",
       "   {'_index': 'movies',\n",
       "    '_type': '_doc',\n",
       "    '_id': '6365',\n",
       "    '_score': 8.245362,\n",
       "    '_source': {'movieId': 6365,\n",
       "     'title': 'Matrix Reloaded, The',\n",
       "     'release_date': '2003',\n",
       "     'genres': ['action', 'adventure', 'sci-fi', 'thriller', 'imax'],\n",
       "     'tmdbId': 604}},\n",
       "   {'_index': 'movies',\n",
       "    '_type': '_doc',\n",
       "    '_id': '6934',\n",
       "    '_score': 8.245362,\n",
       "    '_source': {'movieId': 6934,\n",
       "     'title': 'Matrix Revolutions, The',\n",
       "     'release_date': '2003',\n",
       "     'genres': ['action', 'adventure', 'sci-fi', 'thriller', 'imax'],\n",
       "     'tmdbId': 605}}]}}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test things out by searching for movies containing \"matrix\" in the title\n",
    "es.search(index=\"movies\", q=\"title:matrix\", size=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Train a recommmender model on the ratings data\n",
    "\n",
    "Your data is now stored in Elasticsearch and you will use the ratings data to build a collaborative filtering recommendation model.\n",
    "\n",
    "[Collaborative filtering](https://en.wikipedia.org/wiki/Collaborative_filtering) is a recommendation approach that is effectively based on the \"wisdom of the crowd\". It makes the assumption that, if two people share similar preferences, then the things that one of them prefers could be good recommendations to make to the other. In other words, if user A tends to like certain movies, and user B shares some of these preferences with user A, then the movies that user A likes, that user B _has not yet seen_, may well be movies that user B will also like.\n",
    "\n",
    "In a similar manner, we can think about _items_ as being similar if they tend to be rated highly by the same people, on average. \n",
    "\n",
    "Hence these models are based on the combined, collaborative preferences and behavior of all users in aggregate. They tend to be very effective in practice (provided you have enough preference data to train the model). The ratings data you have is a form of _explicit preference data_, perfect for training collaborative filtering models.\n",
    "\n",
    "### Alternating Least Squares\n",
    "\n",
    "Alternating Least Squares (ALS) is a specific algorithm for solving a type of collaborative filtering model known as [matrix factorization (MF)](https://en.wikipedia.org/wiki/Matrix_decomposition). The core idea of MF is to represent the ratings as a _user-item ratings matrix_. In the diagram below you will see this matrix on the left (with users as _rows_ and movies as _columns_). The entries in this matrix are the ratings given by users to movies.\n",
    "\n",
    "You may also notice that the matrix has _missing entries_ because not all users have rated all movies. In this situation we refer to the data as _sparse_.\n",
    "\n",
    "![als-diagram.png](../doc/source/images/als-diagram.png)\n",
    "\n",
    "MF methods aim to find two much smaller matrices (one representing the _users_ and the other the _items_) that, when multiplied together, re-construct the original ratings matrix as closely as possible. This is know as _factorizing_ the original matrix, hence the name of the technique.\n",
    "\n",
    "The two smaller matrices are called _factor matrices_ (or _latent features_). The user and movie factor matrices are illustrated on the right in the diagram above. The idea is that each user factor vector is a compressed representation of the user's preferences and behavior. Likewise, each item factor vector is a compressed representation of the item. Once the model is trained, the factor vectors can be used to make recommendations, which is what you will do in the following sections.\n",
    "\n",
    "__Further reading:__\n",
    "\n",
    "* [Spark MLlib Collaborative Filtering](http://spark.apache.org/docs/latest/ml-collaborative-filtering.html)\n",
    "* [Alternating Least Squares and collaborative filtering](https://datasciencemadesimpler.wordpress.com/tag/alternating-least-squares/)\n",
    "* [Quora question on Alternating Least Squares](https://www.quora.com/What-is-the-Alternating-Least-Squares-method-in-recommendation-systems-And-why-does-this-algorithm-work-intuition-behind-this)\n",
    "\n",
    "Fortunately, Spark's MLlib machine learning library has a scalable, efficient implementation of matrix factorization built in, which we can use to train our recommendation model. Next, you will use Spark's ALS to train a model on your ratings data from Elasticsearch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------+-------------------+------+\n",
      "|movieId|rating|          timestamp|userId|\n",
      "+-------+------+-------------------+------+\n",
      "|      1|   4.0|2000-07-30 20:45:03|     1|\n",
      "|      3|   4.0|2000-07-30 20:20:47|     1|\n",
      "|      6|   4.0|2000-07-30 20:37:04|     1|\n",
      "|     47|   5.0|2000-07-30 21:03:35|     1|\n",
      "|     50|   5.0|2000-07-30 20:48:51|     1|\n",
      "+-------+------+-------------------+------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ratings_from_es = spark.read.format(\"es\").load(\"ratings\")\n",
    "ratings_from_es.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+--------------------+\n",
      "| id|            features|\n",
      "+---+--------------------+\n",
      "| 10|[0.13299428, 0.42...|\n",
      "| 20|[0.2196542, -0.28...|\n",
      "| 30|[-0.58235997, 0.1...|\n",
      "| 40|[0.31613937, -0.2...|\n",
      "| 50|[0.3011615, 0.112...|\n",
      "+---+--------------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "+---+--------------------+\n",
      "| id|            features|\n",
      "+---+--------------------+\n",
      "| 10|[0.6092093, 0.570...|\n",
      "| 20|[0.12555358, -0.2...|\n",
      "| 30|[0.9184103, 0.465...|\n",
      "| 40|[0.40230548, 0.53...|\n",
      "| 50|[0.56450856, 0.10...|\n",
      "+---+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.recommendation import ALS\n",
    "from pyspark.sql.functions import col\n",
    "als = ALS(userCol=\"userId\", itemCol=\"movieId\", ratingCol=\"rating\", regParam=0.02, rank=VECTOR_DIM, seed=54)\n",
    "model = als.fit(ratings_from_es)\n",
    "model.userFactors.show(5)\n",
    "model.itemFactors.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Export ALS user and item factor vectors to Elasticsearch\n",
    "\n",
    "Congratulations, you've trained a recommendation model! The next step is to export the model factors (shown in the `DataFrames` above) to Elasticsearch.\n",
    "\n",
    "We can export the model factor vector directly to Elasticsearch, since it is an array and the `dense_vector` field expects an array as input.\n",
    "\n",
    "For illustrative purposes, we will also export model metadata (such as the Spark model id and a timestamp)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write the model factor vectors, model version and model timestamp to Elasticsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+--------------------+----------------+---------------+\n",
      "| id|        model_factor|   model_version|model_timestamp|\n",
      "+---+--------------------+----------------+---------------+\n",
      "| 10|[0.6092093, 0.570...|ALS_b289ae1d349c|     1585732983|\n",
      "| 20|[0.12555358, -0.2...|ALS_b289ae1d349c|     1585732983|\n",
      "| 30|[0.9184103, 0.465...|ALS_b289ae1d349c|     1585732983|\n",
      "| 40|[0.40230548, 0.53...|ALS_b289ae1d349c|     1585732983|\n",
      "| 50|[0.56450856, 0.10...|ALS_b289ae1d349c|     1585732983|\n",
      "+---+--------------------+----------------+---------------+\n",
      "only showing top 5 rows\n",
      "\n",
      "+---+--------------------+----------------+---------------+\n",
      "| id|        model_factor|   model_version|model_timestamp|\n",
      "+---+--------------------+----------------+---------------+\n",
      "| 10|[0.13299428, 0.42...|ALS_b289ae1d349c|     1585732983|\n",
      "| 20|[0.2196542, -0.28...|ALS_b289ae1d349c|     1585732983|\n",
      "| 30|[-0.58235997, 0.1...|ALS_b289ae1d349c|     1585732983|\n",
      "| 40|[0.31613937, -0.2...|ALS_b289ae1d349c|     1585732983|\n",
      "| 50|[0.3011615, 0.112...|ALS_b289ae1d349c|     1585732983|\n",
      "+---+--------------------+----------------+---------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import lit, current_timestamp, unix_timestamp\n",
    "ver = model.uid\n",
    "ts = unix_timestamp(current_timestamp())\n",
    "movie_vectors = model.itemFactors.select(\"id\",\\\n",
    "                                         col(\"features\").alias(\"model_factor\"),\\\n",
    "                                         lit(ver).alias(\"model_version\"),\\\n",
    "                                         ts.alias(\"model_timestamp\"))\n",
    "movie_vectors.show(5)\n",
    "user_vectors = model.userFactors.select(\"id\",\\\n",
    "                                        col(\"features\").alias(\"model_factor\"),\\\n",
    "                                        lit(ver).alias(\"model_version\"),\\\n",
    "                                        ts.alias(\"model_timestamp\"))\n",
    "user_vectors.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# write data to ES, use:\n",
    "# - \"id\" as the column to map to ES movie id\n",
    "# - \"update\" write mode for ES, since you want to update new fields only\n",
    "# - \"append\" write mode for Spark\n",
    "movie_vectors.write.format(\"es\") \\\n",
    "    .option(\"es.mapping.id\", \"id\") \\\n",
    "    .option(\"es.write.operation\", \"update\") \\\n",
    "    .save(\"movies\", mode=\"append\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# write data to ES, use:\n",
    "# - \"id\" as the column to map to ES movie id\n",
    "# - \"index\" write mode for ES, since you have not written to the user index previously\n",
    "# - \"append\" write mode for Spark\n",
    "user_vectors.write.format(\"es\") \\\n",
    "    .option(\"es.mapping.id\", \"id\") \\\n",
    "    .option(\"es.write.operation\", \"index\") \\\n",
    "    .save(\"users\", mode=\"append\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the data  was written correctly\n",
    "\n",
    "You can search for a movie to see if the model factor vector was written correctly. You should see a `'model_factor': [0..188..., ]` field in the returned movie document, as well as a `model_version` and `model_timestamp` field)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_index': 'movies',\n",
       " '_type': '_doc',\n",
       " '_id': '122886',\n",
       " '_score': 10.524785,\n",
       " '_source': {'movieId': 122886,\n",
       "  'title': 'Star Wars: Episode VII - The Force Awakens',\n",
       "  'release_date': '2015',\n",
       "  'genres': ['action', 'adventure', 'fantasy', 'sci-fi', 'imax'],\n",
       "  'tmdbId': 140607,\n",
       "  'model_timestamp': 1585732985,\n",
       "  'model_version': 'ALS_b289ae1d349c',\n",
       "  'model_factor': [0.18812202,\n",
       "   0.7076669,\n",
       "   -1.3437241,\n",
       "   -0.3083917,\n",
       "   0.31944987,\n",
       "   0.45389917,\n",
       "   -0.8027336,\n",
       "   -0.66270983,\n",
       "   1.2934446,\n",
       "   -0.12376652,\n",
       "   0.5889607,\n",
       "   -0.12555946,\n",
       "   -1.1881688,\n",
       "   0.60742664,\n",
       "   1.0457083,\n",
       "   0.564867,\n",
       "   0.89669603,\n",
       "   1.1521518,\n",
       "   -0.39917782,\n",
       "   -2.687924],\n",
       "  'id': 122886}}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# search for a particular sci-fi movie\n",
    "es.search(index=\"movies\", q=\"force awakens\")['hits']['hits'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Recommend using Elasticsearch!\n",
    "\n",
    "Now that you have loaded your recommendation model into Elasticsearch, you will generate some recommendations.\n",
    "First, you will need to create a few utility functions for:\n",
    "\n",
    "* Fetching movie posters from TMdb API (optional)\n",
    "* Constructing the Elasticsearch [script score query for vector functions](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-script-score-query.html#vector-functions) to generate recommendations from your factor model\n",
    "* Given a movie, use this query to find the movies most similar to it\n",
    "* Given a user, use this query to find the movies with the highest predicted rating, to recommend to the user\n",
    "* Display the results as an HTML table in Jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image, HTML, display\n",
    "\n",
    "def get_poster_url(id):\n",
    "    \"\"\"Fetch movie poster image URL from TMDb API given a tmdbId\"\"\"\n",
    "    \n",
    "    IMAGE_URL = 'https://image.tmdb.org/t/p/w500'\n",
    "    try:\n",
    "        import tmdbsimple as tmdb\n",
    "        from tmdbsimple import APIKeyError\n",
    "        try:\n",
    "            movie = tmdb.Movies(id).info()\n",
    "            poster_url = IMAGE_URL + movie['poster_path'] if 'poster_path' in movie and movie['poster_path'] is not None else \"\"\n",
    "            return poster_url\n",
    "        except APIKeyError as ae:\n",
    "            return \"KEY_ERR\"\n",
    "    except Exception as me:\n",
    "        return \"NA\"\n",
    "    \n",
    "    \n",
    "def vector_query(query_vec, vector_field, q=\"*\", cosine=False):\n",
    "    \"\"\"\n",
    "    Construct an Elasticsearch script score query using `dense_vector` fields\n",
    "    \n",
    "    The script score query takes as parameters the query vector (as a Python list)\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    query_vec : list\n",
    "        The query vector\n",
    "    vector_field : str\n",
    "        The field name in the document against which to score `query_vec`\n",
    "    q : str, optional\n",
    "        Query string for the search query (default: '*' to search across all documents)\n",
    "    cosine : bool, optional\n",
    "        Whether to compute cosine similarity. If `False` then the dot product is computed (default: False)\n",
    "     \n",
    "    Note: Elasticsearch cannot rank negative scores. Therefore, in the case of the dot product, a sigmoid transform\n",
    "    is applied. In the case of cosine similarity, 1.0 is added to the score. In both cases, documents with no \n",
    "    factor vectors are ignored by applying a 0.0 score.\n",
    "    \n",
    "    The query vector passed in will be the user factor vector (if generating recommended movies for a user)\n",
    "    or movie factor vector (if generating similar movies for a given movie)\n",
    "    \"\"\"\n",
    "    \n",
    "    if cosine:\n",
    "        score_fn = \"doc['{v}'].size() == 0 ? 0 : cosineSimilarity(params.vector, '{v}') + 1.0\"\n",
    "    else:\n",
    "        score_fn = \"doc['{v}'].size() == 0 ? 0 : sigmoid(1, Math.E, -dotProduct(params.vector, '{v}'))\"\n",
    "       \n",
    "    score_fn = score_fn.format(v=vector_field, fn=score_fn)\n",
    "    \n",
    "    return {\n",
    "    \"query\": {\n",
    "        \"script_score\": {\n",
    "            \"query\" : { \n",
    "                \"query_string\": {\n",
    "                    \"query\": q\n",
    "                }\n",
    "            },\n",
    "            \"script\": {\n",
    "                \"source\": score_fn,\n",
    "                \"params\": {\n",
    "                    \"vector\": query_vec\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "\n",
    "def get_similar(the_id, q=\"*\", num=10, index=\"movies\", vector_field='model_factor'):\n",
    "    \"\"\"\n",
    "    Given a movie id, execute the recommendation script score query to find similar movies,\n",
    "    ranked by cosine similarity. We return the `num` most similar, excluding the movie itself.\n",
    "    \"\"\"\n",
    "    response = es.get(index=index, id=the_id)\n",
    "    src = response['_source']\n",
    "    if vector_field in src:\n",
    "        query_vec = src[vector_field]\n",
    "        q = vector_query(query_vec, vector_field, q=q, cosine=True)\n",
    "        results = es.search(index=index, body=q)\n",
    "        hits = results['hits']['hits']\n",
    "        return src, hits[1:num+1]\n",
    "    \n",
    "    \n",
    "def get_user_recs(the_id, q=\"*\", num=10, users=\"users\", movies=\"movies\", vector_field='model_factor'):\n",
    "    \"\"\"\n",
    "    Given a user id, execute the recommendation script score query to find top movies, ranked by predicted rating\n",
    "    \"\"\"\n",
    "    response = es.get(index=users, id=the_id)\n",
    "    src = response['_source']\n",
    "    if vector_field in src:\n",
    "        query_vec = src[vector_field]\n",
    "        q = vector_query(query_vec, vector_field, q=q, cosine=False)\n",
    "        results = es.search(index=movies, body=q)\n",
    "        hits = results['hits']['hits']\n",
    "        return src, hits[:num]\n",
    "\n",
    "def get_movies_for_user(the_id, num=10, ratings=\"ratings\", movies=\"movies\"):\n",
    "    \"\"\"\n",
    "    Given a user id, get the movies rated by that user, from highest- to lowest-rated.\n",
    "    \"\"\"\n",
    "    response = es.search(index=ratings, q=\"userId:{}\".format(the_id), size=num, sort=[\"rating:desc\"])\n",
    "    hits = response['hits']['hits']\n",
    "    ids = [h['_source']['movieId'] for h in hits]\n",
    "    movies = es.mget(body={\"ids\": ids}, index=movies, _source_includes=['tmdbId', 'title'])\n",
    "    movies_hits = movies['docs']\n",
    "    tmdbids = [h['_source'] for h in movies_hits]\n",
    "    return tmdbids\n",
    "\n",
    "            \n",
    "def display_user_recs(the_id, q=\"*\", num=10, num_last=10, users=\"users\", movies=\"movies\", ratings=\"ratings\"):\n",
    "    user, recs = get_user_recs(the_id, q, num, users, movies)\n",
    "    user_movies = get_movies_for_user(the_id, num_last, ratings, movies)\n",
    "    # check that posters can be displayed\n",
    "    first_movie = user_movies[0]\n",
    "    first_im_url = get_poster_url(first_movie['tmdbId'])\n",
    "    if first_im_url == \"NA\":\n",
    "        display(HTML(\"<i>Cannot import tmdbsimple. No movie posters will be displayed!</i>\"))\n",
    "    if first_im_url == \"KEY_ERR\":\n",
    "        display(HTML(\"<i>Key error accessing TMDb API. Check your API key. No movie posters will be displayed!</i>\"))\n",
    "        \n",
    "    # display the movies that this user has rated highly\n",
    "    display(HTML(\"<h2>Get recommended movies for user id %s</h2>\" % the_id))\n",
    "    display(HTML(\"<h4>The user has rated the following movies highly:</h4>\"))\n",
    "    user_html = \"<table border=0>\"\n",
    "    i = 0\n",
    "    for movie in user_movies:\n",
    "        movie_im_url = get_poster_url(movie['tmdbId'])\n",
    "        movie_title = movie['title']\n",
    "        user_html += \"<td><h5>%s</h5><img src=%s width=150></img></td>\" % (movie_title, movie_im_url)\n",
    "        i += 1\n",
    "        if i % 5 == 0:\n",
    "            user_html += \"</tr><tr>\"\n",
    "    user_html += \"</tr></table>\"\n",
    "    display(HTML(user_html))\n",
    "    # now display the recommended movies for the user\n",
    "    display(HTML(\"<br>\"))\n",
    "    display(HTML(\"<h2>Recommended movies:</h2>\"))\n",
    "    rec_html = \"<table border=0>\"\n",
    "    i = 0\n",
    "    for rec in recs:\n",
    "        r_im_url = get_poster_url(rec['_source']['tmdbId'])\n",
    "        r_score = rec['_score']\n",
    "        r_title = rec['_source']['title']\n",
    "        rec_html += \"<td><h5>%s</h5><img src=%s width=150></img></td><td><h5>%2.3f</h5></td>\" % (r_title, r_im_url, r_score)\n",
    "        i += 1\n",
    "        if i % 5 == 0:\n",
    "            rec_html += \"</tr><tr>\"\n",
    "    rec_html += \"</tr></table>\"\n",
    "    display(HTML(rec_html))\n",
    "\n",
    "    \n",
    "def display_similar(the_id, q=\"*\", num=10, movies=\"movies\"):\n",
    "    \"\"\"\n",
    "    Display query movie, together with similar movies and similarity scores, in a table\n",
    "    \"\"\"\n",
    "    movie, recs = get_similar(the_id, q, num, movies)\n",
    "    q_im_url = get_poster_url(movie['tmdbId'])\n",
    "    if q_im_url == \"NA\":\n",
    "        display(HTML(\"<i>Cannot import tmdbsimple. No movie posters will be displayed!</i>\"))\n",
    "    if q_im_url == \"KEY_ERR\":\n",
    "        display(HTML(\"<i>Key error accessing TMDb API. Check your API key. No movie posters will be displayed!</i>\"))\n",
    "        \n",
    "    display(HTML(\"<h2>Get similar movies for:</h2>\"))\n",
    "    display(HTML(\"<h4>%s</h4>\" % movie['title']))\n",
    "    if q_im_url != \"NA\":\n",
    "        display(Image(q_im_url, width=200))\n",
    "    display(HTML(\"<br>\"))\n",
    "    display(HTML(\"<h2>People who liked this movie also liked these:</h2>\"))\n",
    "    sim_html = \"<table border=0>\"\n",
    "    i = 0\n",
    "    for rec in recs:\n",
    "        r_im_url = get_poster_url(rec['_source']['tmdbId'])\n",
    "        r_score = rec['_score']\n",
    "        r_title = rec['_source']['title']\n",
    "        sim_html += \"<td><h5>%s</h5><img src=%s width=150></img></td><td><h5>%2.3f</h5></td>\" % (r_title, r_im_url, r_score)\n",
    "        i += 1\n",
    "        if i % 5 == 0:\n",
    "            sim_html += \"</tr><tr>\"\n",
    "    sim_html += \"</tr></table>\"\n",
    "    display(HTML(sim_html))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, you're ready to generate some recommendations.\n",
    "\n",
    "### 5(a) Find similar movies for a given movie\n",
    "\n",
    "To start, you can find movies that are _similar_ to a given movie. This similarity score is computed from the model factor vectors for each movie. Recall that the ALS model you trained earlier is a collaborative filtering model, so the similarity between movie vectors will be based on the _rating co-occurrence_ of the movies. In other words, two movies that tend to be rated highly by a user will tend to be more similar. It is common to use the [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) of the movie factor vectors as a measure of the similarity between two movies.\n",
    "\n",
    "Using this similarity you can show recommendations along the lines of _people who liked this movie also liked these_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Get similar movies for:</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h4>Godfather, The</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 200
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h2>People who liked this movie also liked these:</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=0><td><h5>Godfather: Part II, The</h5><img src=https://image.tmdb.org/t/p/w500/bVq65huQ8vHDd1a4Z37QtuyEvpA.jpg width=150></img></td><td><h5>1.993</h5></td><td><h5>Apocalypse Now</h5><img src=https://image.tmdb.org/t/p/w500/jcvJ2xcVWU9Wh0hZAxcs103s8nN.jpg width=150></img></td><td><h5>1.963</h5></td><td><h5>Goodfellas</h5><img src=https://image.tmdb.org/t/p/w500/pwpGfTImTGifEGgLb3s6LRPd4I6.jpg width=150></img></td><td><h5>1.961</h5></td><td><h5>Get Shorty</h5><img src=https://image.tmdb.org/t/p/w500/vWtDUUgQAsVyvRW4mE75LBgVm2e.jpg width=150></img></td><td><h5>1.959</h5></td><td><h5>Reservoir Dogs</h5><img src=https://image.tmdb.org/t/p/w500/g7spS2Y4SZoQoC6Hn7zoqEqdYqR.jpg width=150></img></td><td><h5>1.957</h5></td></tr><tr></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_similar(858, num=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we see that people who like the original Godfather movie tend like other crime drama movies (including another Godfather film), as well as some general drama movies.\n",
    "\n",
    "> _Note_ since we are using a relatively small and sparse dataset, results may not be as good as those for the same model trained on a larger dataset.\n",
    "\n",
    "Now you will see the power and flexibility that comes from using a search engine to generate recommendations. Elasticsearch allows you to tweak the results returned by the recommendation query using any standard search query or filter - from free text search through to filters based on time and geo-location (or any other piece of metadata you can think of).\n",
    "\n",
    "#### Filter recommendations based on title\n",
    "\n",
    "For example, perhaps you want to remove any movies with \"godfather\" in the title from the recommendations. You can do this by simply passing a valid Elasticsearch query string to the recommendation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Get similar movies for:</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h4>Godfather, The</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "width": 200
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<br>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h2>People who liked this movie also liked these:</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=0><td><h5>Goodfellas</h5><img src=https://image.tmdb.org/t/p/w500/pwpGfTImTGifEGgLb3s6LRPd4I6.jpg width=150></img></td><td><h5>1.961</h5></td><td><h5>Get Shorty</h5><img src=https://image.tmdb.org/t/p/w500/vWtDUUgQAsVyvRW4mE75LBgVm2e.jpg width=150></img></td><td><h5>1.959</h5></td><td><h5>Reservoir Dogs</h5><img src=https://image.tmdb.org/t/p/w500/g7spS2Y4SZoQoC6Hn7zoqEqdYqR.jpg width=150></img></td><td><h5>1.957</h5></td><td><h5>All the President's Men</h5><img src=https://image.tmdb.org/t/p/w500/cPtSHR7D2WGsDBfnC5DxV927hKn.jpg width=150></img></td><td><h5>1.954</h5></td><td><h5>Scarface</h5><img src=https://image.tmdb.org/t/p/w500/zr2p353wrd6j3wjLgDT4TcaestB.jpg width=150></img></td><td><h5>1.952</h5></td></tr><tr></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_similar(858, num=5, q=\"title:(NOT godfather)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that we now only have similar movies that are _not also part of the Godfather trilogy_.\n",
    "\n",
    "#### Filter recommendations based on genre\n",
    "\n",
    "Or you may want to ensure that only valid children's movies are shown to young viewers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Get similar movies for:</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h4>Toy Story</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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     "data": {
      "text/html": [
       "<table border=0><td><h5>Monsters, Inc.</h5><img src=https://image.tmdb.org/t/p/w500/93Y9BGx8blzmZOPSoivkFfaifqU.jpg width=150></img></td><td><h5>1.966</h5></td><td><h5>Incredibles, The</h5><img src=https://image.tmdb.org/t/p/w500/2LqaLgk4Z226KkgPJuiOQ58wvrm.jpg width=150></img></td><td><h5>1.961</h5></td><td><h5>Aladdin</h5><img src=https://image.tmdb.org/t/p/w500/mjKozYRuHc9j7SmiXmbVmCiAM0A.jpg width=150></img></td><td><h5>1.957</h5></td><td><h5>Finding Nemo</h5><img src=https://image.tmdb.org/t/p/w500/syPWyeeqzTQIxjIUaIFI7d0TyEY.jpg width=150></img></td><td><h5>1.956</h5></td><td><h5>Wallace & Gromit: The Wrong Trousers</h5><img src=https://image.tmdb.org/t/p/w500/O3fFWazkIL1QrmH5t9numsUgmR.jpg width=150></img></td><td><h5>1.950</h5></td></tr><tr></tr></table>"
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   "source": [
    "display_similar(1, num=5, q=\"genres:children\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feel free to check out the documentation for the Elasticsearch [query string query](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-query-string-query.html) and play around with the various queries you can construct by passing in a query string as `q` in the recommendation function above!"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 5(b) Find movies to recommend to a user\n",
    "\n",
    "Now, you're ready to generate some movie recommendations, personalized for a specific user.\n",
    "\n",
    "Given a user, you can recommend movies to that user based on the predicted ratings from your model. In the same manner as for the similar movie recommendations, this predicted rating score is computed from the model factor vector for the user and the factor vectors for each movie. Recall that the collaborative filtering model means that, at a high level, we will recommend movies _liked by other users who liked the same movies as the given user_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Get recommended movies for user id 72</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<h4>The user has rated the following movies highly:</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<table border=0><td><h5>Star Wars: Episode IV - A New Hope</h5><img src=https://image.tmdb.org/t/p/w500/btTdmkgIvOi0FFip1sPuZI2oQG6.jpg width=150></img></td><td><h5>Shawshank Redemption, The</h5><img src=https://image.tmdb.org/t/p/w500/9O7gLzmreU0nGkIB6K3BsJbzvNv.jpg width=150></img></td><td><h5>Matrix, The</h5><img src=https://image.tmdb.org/t/p/w500/hEpWvX6Bp79eLxY1kX5ZZJcme5U.jpg width=150></img></td><td><h5>Amelie (Fabuleux destin d'Amélie Poulain, Le)</h5><img src=https://image.tmdb.org/t/p/w500/f0uorE7K7ggHfr8r7pUTOHWkOlE.jpg width=150></img></td><td><h5>Twelve Monkeys (a.k.a. 12 Monkeys)</h5><img src=https://image.tmdb.org/t/p/w500/6Sj9wDu3YugthXsU0Vry5XFAZGg.jpg width=150></img></td></tr><tr></tr></table>"
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       "<br>"
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<h2>Recommended movies:</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<table border=0><td><h5>Dallas Buyers Club</h5><img src=https://image.tmdb.org/t/p/w500/nxJFUxDRP9qQCiyD5sH24N5SCSu.jpg width=150></img></td><td><h5>0.993</h5></td><td><h5>Memento</h5><img src=https://image.tmdb.org/t/p/w500/fQMSaP88cf1nz4qwuNEEFtazuDM.jpg width=150></img></td><td><h5>0.993</h5></td><td><h5>Adaptation</h5><img src=https://image.tmdb.org/t/p/w500/qP4LbKYVRWw5j1n55sSjvvgmedM.jpg width=150></img></td><td><h5>0.993</h5></td><td><h5>Sin City</h5><img src=https://image.tmdb.org/t/p/w500/vKJVGOKPyWqp9d2EX5NH1liqRqR.jpg width=150></img></td><td><h5>0.993</h5></td><td><h5>Matrix, The</h5><img src=https://image.tmdb.org/t/p/w500/hEpWvX6Bp79eLxY1kX5ZZJcme5U.jpg width=150></img></td><td><h5>0.993</h5></td></tr><tr></tr></table>"
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   ],
   "source": [
    "display_user_recs(72, num=5, num_last=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, note that since we are using a relatively small and sparse dataset, the results may not be too good. However, we can see that this user seems to like some sci-fi and some drama films. The recommended movies fall broadly into these categories and seem to be somewhat reasonable.\n",
    "\n",
    "#### Filter recommendations based on release date\n",
    "\n",
    "Next, you can again apply the power of Elasticsearch's filtering capabilities to your recommendation engine. Let's say you only want to recommend more recent movies (say, from the past 3 years). This can be done by adding a date math query to the recommendation function score query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Get recommended movies for user id 72</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h4>The user has rated the following movies highly:</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=0><td><h5>Star Wars: Episode IV - A New Hope</h5><img src=https://image.tmdb.org/t/p/w500/btTdmkgIvOi0FFip1sPuZI2oQG6.jpg width=150></img></td><td><h5>Shawshank Redemption, The</h5><img src=https://image.tmdb.org/t/p/w500/9O7gLzmreU0nGkIB6K3BsJbzvNv.jpg width=150></img></td><td><h5>Matrix, The</h5><img src=https://image.tmdb.org/t/p/w500/hEpWvX6Bp79eLxY1kX5ZZJcme5U.jpg width=150></img></td><td><h5>Amelie (Fabuleux destin d'Amélie Poulain, Le)</h5><img src=https://image.tmdb.org/t/p/w500/f0uorE7K7ggHfr8r7pUTOHWkOlE.jpg width=150></img></td><td><h5>Twelve Monkeys (a.k.a. 12 Monkeys)</h5><img src=https://image.tmdb.org/t/p/w500/6Sj9wDu3YugthXsU0Vry5XFAZGg.jpg width=150></img></td></tr><tr></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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    {
     "data": {
      "text/html": [
       "<br>"
      ],
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<h2>Recommended movies:</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=0><td><h5>Three Billboards Outside Ebbing, Missouri</h5><img src=https://image.tmdb.org/t/p/w500/vgvw6w1CtcFkuXXn004S5wQsHRl.jpg width=150></img></td><td><h5>0.990</h5></td><td><h5>Logan</h5><img src=https://image.tmdb.org/t/p/w500/fnbjcRDYn6YviCcePDnGdyAkYsB.jpg width=150></img></td><td><h5>0.989</h5></td><td><h5>Split</h5><img src=https://image.tmdb.org/t/p/w500/rXMWOZiCt6eMX22jWuTOSdQ98bY.jpg width=150></img></td><td><h5>0.988</h5></td><td><h5>Baby Driver</h5><img src=https://image.tmdb.org/t/p/w500/rmnQ9jKW72bHu8uKlMjPIb2VLMI.jpg width=150></img></td><td><h5>0.987</h5></td><td><h5>Deadpool 2</h5><img src=https://image.tmdb.org/t/p/w500/to0spRl1CMDvyUbOnbb4fTk3VAd.jpg width=150></img></td><td><h5>0.985</h5></td></tr><tr></tr></table>"
      ],
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   ],
   "source": [
    "display_user_recs(72, num=5, num_last=5, q=\"release_date:[2017 TO *]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the recommendation include only recent movies, again including mostly drama and sci-fi / fantasy titles.\n",
    "\n",
    "As you did with the similar movies recommendations, feel free to play around with the various queries you could pass into the user recommendation query."
   ]
  }
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